opus-mt-en-de
Table of Contents
- Model Details
- Uses
- Risks, Limitations and Biases
- Training
- Evaluation
- Citation Information
- How to Get Started With the Model
Model Details
Model Description:
- Developed by: Language Technology Research Group at the University of Helsinki
- Model Type: Translation
- Language(s):
- Source Language: English
- Target Language: German
- License: CC-BY-4.0
- Resources for more information:
Uses
Direct Use
This model can be used for translation and text-to-text generation.
Risks, Limitations and Biases
CONTENT WARNING: Readers should be aware this section contains content that is disturbing, offensive, and can propagate historical and current stereotypes.
Significant research has explored bias and fairness issues with language models (see, e.g., Sheng et al. (2021) and Bender et al. (2021)).
Further details about the dataset for this model can be found in the OPUS readme: en-de
Training Data
Preprocessing
-
pre-processing: normalization + SentencePiece
-
dataset: opus
-
download original weights: opus-2020-02-26.zip
-
test set translations: opus-2020-02-26.test.txt
Evaluation
Results
- test set scores: opus-2020-02-26.eval.txt
Benchmarks
| testset | BLEU | chr-F |
|---|---|---|
| newssyscomb2009.en.de | 23.5 | 0.540 |
| news-test2008.en.de | 23.5 | 0.529 |
| newstest2009.en.de | 22.3 | 0.530 |
| newstest2010.en.de | 24.9 | 0.544 |
| newstest2011.en.de | 22.5 | 0.524 |
| newstest2012.en.de | 23.0 | 0.525 |
| newstest2013.en.de | 26.9 | 0.553 |
| newstest2015-ende.en.de | 31.1 | 0.594 |
| newstest2016-ende.en.de | 37.0 | 0.636 |
| newstest2017-ende.en.de | 29.9 | 0.586 |
| newstest2018-ende.en.de | 45.2 | 0.690 |
| newstest2019-ende.en.de | 40.9 | 0.654 |
| Tatoeba.en.de | 47.3 | 0.664 |
Citation Information
@InProceedings{TiedemannThottingal:EAMT2020,
author = {J{\"o}rg Tiedemann and Santhosh Thottingal},
title = {{OPUS-MT} — {B}uilding open translation services for the {W}orld},
booktitle = {Proceedings of the 22nd Annual Conferenec of the European Association for Machine Translation (EAMT)},
year = {2020},
address = {Lisbon, Portugal}
}
How to Get Started With the Model
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
tokenizer = AutoTokenizer.from_pretrained("Helsinki-NLP/opus-mt-en-de")
model = AutoModelForSeq2SeqLM.from_pretrained("Helsinki-NLP/opus-mt-en-de")